Application of DEO Method to Solving Fuzzy Multiobjective Optimal Control Problem

In the present paper a problem of optimal control for a single-product dynamical macroeconomic model is considered. In this model gross domestic product is divided into productive consumption, gross investment, and nonproductive consumption. The model is described by a fuzzy differential equation (FDE) to take into account imprecision inherent in the dynamics that may be naturally conditioned by influence of various external factors, unforeseen contingencies of future, and so forth. The considered problems are characterized by four criteria and by several important aspects. On one hand, the problem is complicated by the presence of fuzzy uncertainty as a result of a natural imprecision inherent in information about dynamics of real-world systems. On the other hand, the number of the criteria is not small and most of them are integral criteria. Due to the above mentioned aspects, solving the considered problem by using convolution of criteria into one criterion would lead to loss of information and also would be counterintuitive and complex. We applied DEO (differential evolution optimization) method to solve the considered problem

Application of Fuzzy Logic in Job Satisfaction Performance Problem

AbstractJob satisfaction has been a popular topic of research for many decades. The interest in this topic has attracted psychologists, management scholars and, more recently, economists. Most of the studies conducted in the area of job satisfaction have been based on statistical methods. However these methods cannot account for the fact that basic facets of job satisfaction, such as Activity, Independence, Variety, Social status, and Supervision-human relations, to name but a few, are evaluated based on perceptions which do not provide precise numeric information. Information supported by perceptions can be processed more adequately by using fuzzy logic. In this paper we suggest fuzzy if-then rules based expert system to describe relations between job factors and overall job satisfaction

Fuzzy Expert System for Rectal Cancer Based on Possibility Measure

Intestinal infections in common and colorectal cancer in particular are quite widely spread and affect modern population in a significant manner. Therefore, they have been objects of intensive scientific research for quite a long time. It is known that the colorectal cancer’s diagnostics can face some difficulties caused by the uncertainties in patients’ health status and disease data. The uncertainty, in common, can be classified as probabilistic or possibilistic (fuzzy). The goal of this chapter is to analyze a fuzzy-rule-based medical expert system for the colorectal cancer’s diagnostics. In the modeling, fuzzy inference based on possibility measure and knowledge extraction based on fuzzy clustering are applied. During the initial stage of the system’s modeling, the applied parameters of colorectal cancer were defined by using clinical data. During the next stage, the soft-computing-based evaluation of the cancer’s factors is performed. During the third stage, the applied fuzzy inference, based on possibility measure, is introduced and supported by the examples. The knowledge base of the modeled system consists of the case data obtained from 100 patients in the course of 3 years by the National Center of Oncology. The effectiveness of the modeled system was checked on the testing subset of 30 diagnoses, and 22 predictions by the expert system were defined as correct